code: def compress_and_move(
      working_directory, output_directory, process_count
  ):
      os.makedirs(output_directory, exist_ok=True)
      original_info_file_path = os.path.join(
          working_directory, "info.json"
      )
      assert os.path.exists(original_info_file_path)
  
      tasks = []
      bucket_file_paths = glob.glob(
          os.path.join(working_directory, "output", f"*.bkt.txt.sorted")
      )
      for bucket_file_path in bucket_file_paths:
          task = (
              process_task,
              (working_directory, output_directory, bucket_file_path),
          )
          tasks.append(task)
  
      pool = TqdmMultiProcessPool(process_count)
  
      def on_done(_):
          return None
  
      def on_error(_):
          return None
  
      global_progress = tqdm(
          total=len(bucket_file_paths), dynamic_ncols=True, unit="file"
      )
      _ = pool.map(global_progress, tasks, on_error, on_done)
  
      shutil.copy(
          original_info_file_path,
          os.path.join(output_directory, "info.json"),
      )
  
file_path: lm-evaluation-harness/scripts/clean_training_data/compress_and_package.py
imports: import glob
  import argparse
  import os
  import subprocess
  import shutil
  from tqdm import tqdm
  from tqdm_multiprocess import TqdmMultiProcessPool
  import logging
  from tqdm_multiprocess.logger import setup_logger_tqdm
label_desc: Use image node_id 2 for calling a global function with example usage: compress_and_move(working_directory, output_directory, process_count) and returns: None, None